论文标题
带有扩散概率模型的视网膜OCT的无监督降解
Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic Model
论文作者
论文摘要
光学相干断层扫描（OCT）是一种普遍的非侵入成像方法，可提供高分辨率的视网膜可视化。但是，其固有的缺陷，斑点的噪声会严重恶化OCT的组织可见性。基于深度学习的方法已被广泛用于图像恢复，但其中大多数需要无噪声参考图像进行监督。在这项研究中，我们提出了一个扩散概率模型，该模型完全不受监督，可以从噪声中学习而不是信号。通过将一系列高斯噪声添加到自融合的OCT B扫描中来定义扩散过程。然后，通过马尔可夫链建模的扩散的反向过程提供了可调的降级水平。我们的实验结果表明，我们的方法可以通过简单的工作管道和少量的培训数据显着提高图像质量。
Optical coherence tomography (OCT) is a prevalent non-invasive imaging method which provides high resolution volumetric visualization of retina. However, its inherent defect, the speckle noise, can seriously deteriorate the tissue visibility in OCT. Deep learning based approaches have been widely used for image restoration, but most of these require a noise-free reference image for supervision. In this study, we present a diffusion probabilistic model that is fully unsupervised to learn from noise instead of signal. A diffusion process is defined by adding a sequence of Gaussian noise to self-fused OCT b-scans. Then the reverse process of diffusion, modeled by a Markov chain, provides an adjustable level of denoising. Our experiment results demonstrate that our method can significantly improve the image quality with a simple working pipeline and a small amount of training data.
